Abstract 650: A machine learning model accurately predicts response to immune checkpoint inhibitors in colorectal cancer using the gut microbiome
Authors: Ayah N. Al-Bzour
Journal: Cancer Research (2023)

Abstract Background: Immune checkpoint inhibitors (ICI) have shown a long-lasting response in many cancers such as colorectal cancer. However, only patients with microsatellite instability (MSI-High) or mismatch repair deficiency (dMMR) benefit from ICI. One of the many factors that can influence ICI-response is the gut microbiome, however its effect remains elusive. Here we aim to investigate the role of the gut microbiome in ICI-response using machine learning (ML). Methods: Clinical and genomic data of ICI-treated CRC patients were retrieved from the KEYNOTE-177 trial. Microbiome abundances from The Cancer Genome Atlas (TCGA) were downloaded from the cBioportal database. A label propagation semi-supervised ML approach was conducted to label patients’ responses to ICI treatment in the TCGA cohort. Tumor mutational burden (TMB), MSI status, treatment, histological subtype, TNM staging and prior chemotherapy were fitted into the semi-supervised model to predict ICI-response in the combination cohort. The normalized abundance of 1406 microbial signatures were regularized using a LASSO model to avoid multicollinearity. Signatures with a non-zero LASSO coefficients were fitted into a supervised ML Random Forest Classification model (RFC) to evaluate their prediction for ICI-response. Samples were split with 80:20 training-testing ratio, and model’s performance was evaluated on the testing set using mean bootstrap estimate, 10-fold cross-validation (CV), and area under curve (AUC). Results: A total of 538 CRC patients from the TCGA and 29 ICI-treat patients from the KEYNOTE-177 trial were fitted into the semi-supervised model to label ICI-response in the TCGA cohort. A total of 95 patients were labeled as responder (R) while 488 patients were labeled as non-responder (NR). Responders had significantly higher TMB levels than NR (mean TMB: 22.5 vs. 12.2, p-value &amp;lt;0.0001). Of the 95 R, 42 were MSS, and 53 patients were MSI-High. While 417 of the NR were MSS. A total of 25 microbial signatures were screened after LASSO for their ICI prediction and were fitted into the RFC model along with TMB and MSI status (Hereafter called RFC27). The RFC27 model performed on the testing set with mean bootstrap estimate of 0.88 and 95%CI: [0.83-0.94], 10-fold CV of 0.84 and AUC of 0.97. TMB and MSI showed highest feature contribution followed by Actinopolyspora and Bacteroides Eggerthia. The relative abundance of both microbiotas was significantly different between R and NR (mean: 0.99 vs. 0.47, 2.0 vs. 1.6; p-value &amp;lt;0.001 respectively). Conclusions: Our results show that the gut microbiome can accurately predict ICI-response and their higher abundance contributed to a better response. Previous studies also reported a pro-inflammatory response of Bacteroides in addition to their association with ICI-response in melanoma patients, thus providing a potential therapeutic marker for ICI. Citation Format: Ayah N. Al-Bzour. A machine learning model accurately predicts response to immune checkpoint inhibitors in colorectal cancer using the gut microbiome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 650.